Securing network resources from known threats

ABSTRACT

The present disclosure relates to securing workloads of a network by identifying compromised elements in communication with the network and preventing their access to network resources. In one aspect, a method includes monitoring network traffic at network elements of a network; detecting a compromised element in communication with one or more of the network elements, the compromised element being associated with at least one network threat; and based on a defined network policy, applying one of a number of different access prevention schemes to the compromised element to prevent access to the network by the compromised element.

CROSS-REFERENCE TO RELATED APPLICATIONS

The instant application is a Continuation of, and claims priority to,U.S. patent application Ser. No. 17/003,364 entitled SECURING NETWORKRESOURCES FROM KNOWN THREATS, filed Aug. 26, 2020, the contents of whichare herein incorporated by reference in its entirety.

TECHNICAL FILED

The subject matter of this disclosure relates in general to the field ofcomputer networks, and more specifically to securing workloads of anetwork by identifying compromised elements in communication with thenetwork and preventing their access to network resources.

BACKGROUND

With expansion of enterprise networks and their applicability,applications and workloads available on such enterprise networks may beaccessed by a various devices. To ensure application and workloadsecurity, enterprises must develop and enforce policies which governaccessibility of network workloads. However, enterprises often lack theinformation which allows them to enforce granular policies for access tospecific applications taking into account devices exposure toun-authorized and malicious external resources.

BRIEF DESCRIPTION OF THE FIGURES

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments that are illustrated inthe appended drawings. Understanding that these drawings depict onlyembodiments of the disclosure and are not therefore to be considered tobe limiting of its scope, the principles herein are described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 illustrates an example of a network traffic monitoring system,according to one aspect of the present disclosure;

FIG. 2 illustrates an example of a network environment, according to oneaspect of the present disclosure;

FIG. 3 illustrates an example of a data pipeline for generating networkinsights based on collected network information, according to one aspectof the present disclosure;

FIG. 4 illustrates a simplified version of a setting in which elementsof network environment of FIG. 2 communicate with potential networkthreats, according to one aspect of the present disclosure;

FIG. 5 illustrates another simplified version of a setting in whichelements of network environment of FIG. 2 communicate with potentialmalicious hosts, according to one aspect of the present disclosure;

FIG. 6 describes a process for processing and fetching a list of knownnetwork threats to network sensors for detecting compromised workloadsand endpoints, according to one aspect of the present disclosure;

FIG. 7 is an example of network monitoring process for preventingnetwork access from malicious sources, according to one aspect of thepresent disclosure; and

FIG. 8 illustrates an example computing system, according to one aspectof the present disclosure.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.Thus, the following description and drawings are illustrative and arenot to be construed as limiting. Numerous specific details are describedto provide a thorough understanding of the disclosure. However, incertain instances, well-known or conventional details are not describedin order to avoid obscuring the description. References to one or anembodiment in the present disclosure can be references to the sameembodiment or any embodiment; and, such references mean at least one ofthe embodiments.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,nor are separate or alternative embodiments mutually exclusive of otherembodiments. Moreover, various features are described which may beexhibited by some embodiments and not by others.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Alternative language andsynonyms may be used for any one or more of the terms discussed herein,and no special significance should be placed upon whether or not a termis elaborated or discussed herein. In some cases, synonyms for certainterms are provided. A recital of one or more synonyms does not excludethe use of other synonyms. The use of examples anywhere in thisspecification including examples of any terms discussed herein isillustrative only, and is not intended to further limit the scope andmeaning of the disclosure or of any example term. Likewise, thedisclosure is not limited to various embodiments given in thisspecification.

Without intent to limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Note thattitles or subtitles may be used in the examples for convenience of areader, which in no way should limit the scope of the disclosure. Unlessotherwise defined, technical and scientific terms used herein have themeaning as commonly understood by one of ordinary skill in the art towhich this disclosure pertains. In the case of conflict, the presentdocument, including definitions will control.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Overview

Disclosed herein are methods, systems, and non-transitorycomputer-readable readable media for securing workloads of a network byidentifying compromised elements in communication with the network andpreventing their access to network resources. More specifically,disclosed are methods, systems and non-transitory computer-readablereadable media for applying a dynamic access prevention scheme tocompromised element(s) in communication with resources inside thenetwork, based on the nature of the underlying threat detected inconnection with the compromised element(s).

In one aspect, a method includes monitoring network traffic at networkelements of a network; detecting a compromised element in communicationwith one or more of the network elements, the compromised element beingassociated with at least one network threat; and based on a definednetwork policy, applying one of a number of different access preventionschemes to the compromised element to prevent access to the network bythe compromised element.

In another aspect, detecting the compromised element includesidentifying the at least one network threat in corresponding networktraffic monitored with respect to at least one network element of thenetwork elements; and marking the at least one network element as thecompromised element.

In another aspect, identifying the at least one network threat includesreceiving a list of known network threats; generating tags foridentifying the known network threats; and identifying the at least onenetwork threat using the tags.

In another aspect, the number of different access prevention schemesinclude blocking the compromised element from accessing at least onenetwork element of the network elements; or quarantining the compromisedelement for a period of time, wherein the quarantining prevents anycommunication to and from the compromised element.

In another aspect, the one of the number of different access preventionschemes includes blocking the compromised element from accessing a firstworkload on one or more of the network elements while allowing thecompromised element to access a second workload on the one or more ofthe network elements.

In another aspect, the network threat is one of a known network IPaddress or a malware category.

In another aspect, the compromised element is an endpoint registeredwith the network, the endpoint having accessed an external source havingthe at least one network threat.

In one aspect, a network element includes one or more memories havingcomputer-readable instructions stored therein and one or moreprocessors. The one or more processors are configured to execute thecomputer-readable instructions to monitor network traffic at networkelements of a network; detect a compromised element in communicationwith one or more of the network elements, the compromised element beingassociated with at least one network threat; and based on a definednetwork policy, apply one of a number of different access preventionschemes to the compromised element to prevent access to the network bythe compromised element.

In one aspect, one or more non-transitory computer-readable mediainclude computer-readable instructions, which when executed by one ormore processors, cause the one or more processors to monitor networktraffic at network elements of a network; detect a compromised elementin communication with one or more of the network elements, thecompromised element being associated with at least one network threat;and based on a defined network policy, apply one of a number ofdifferent access prevention schemes to the compromised element toprevent access to the network by the compromised element.

Description

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustrative purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without departing from the spirit and scope of thedisclosure.

The disclosed technology addresses the need in the art for ensuringnetwork security by detecting compromised workloads and endpoints(compromised elements) and preventing partial or complete access to orfrom the compromised elements, by resources of the network. As will bedescribed below and depending on the nature of the threat detected inconnection with a compromised element, a different access preventionscheme may be applied wherein the compromised element can be blockedpartially from accessing particular workload(s) in the network,particular node(s) or be completely blocked by being quarantined.Detection of compromised elements may be based on identification ofthreats using a list of known threats (e.g., known Cyber Threat Alliance(CTA) threats) that identify malicious sources, malicious categories ofthreats, malicious Universal Resource Locators (URLs), malicious IPs.Such list may be distributed to agents deployed on network nodes thatare configured to monitor network traffic and detect the threats.

The present technologies will be described in more detail in thedisclosure as follows. The disclosure begins with an initial discussionof systems and technologies for monitoring network activity in a networkenvironment with respect to FIGS. 1-3 . The discussion will continuewith examples of settings in which network elements may be exposed tonetwork threats (FIGS. 4 and 5 ) followed by processes for setting up aprocess to identify the network threats and implement a dynamic accessprevention scheme when threats are identified (FIGS. 6 and 7 ). Thediscussion will conclude with examples of system and deviceconfigurations and architectures that can be utilized in the context ofthe present disclosure as various elements of systems of FIGS. 1-5 .

Sensors deployed in a network can be used to gather network informationrelated to network traffic of nodes operating in the network and processinformation for nodes and applications running in the network. Gatherednetwork information can be analyzed to provide insights into theoperation of the nodes in the network, otherwise referred to asanalytics. In particular, discovered applications or inventories,application dependencies, policies, efficiencies, resource and bandwidthusage, and network flows can be determined for the network using thenetwork traffic data. For example, an analytics engine can be configuredto automate discovery of applications running in the network, map theapplications' interdependencies, or generate a set of proposed networkpolicies for implementation.

The analytics engine can monitor network information, processinformation, and other relevant information of traffic passing throughthe network using a sensor network that provides multiple perspectivesfor the traffic. The sensor network can include sensors for networkingdevices (e.g., routers, switches, network appliances), physical servers,hypervisors or shared kernels, and virtual partitions (e.g., VMs orcontainers), and other network elements. The analytics engine cananalyze the network information, process information, and otherpertinent information to determine various network insights.

FIG. 1 illustrates an example of a network traffic monitoring system,according to one aspect of the present disclosure.

The network traffic monitoring system 100 can include a configurationmanager 102, sensors 104, a collector module 106, a data mover module108, an analytics engine 110, and a presentation module 112. In FIG. 1 ,the analytics engine 110 is also shown in communication with out-of-banddata sources 114, third party data sources 116, and a network controller118.

The configuration manager 102 can be used to provision and maintain thesensors 104, including installing sensor software or firmware in variousnodes of a network, configuring the sensors 104, updating the sensorsoftware or firmware, among other sensor management tasks. For example,the sensors 104 can be implemented as virtual partition images (e.g.,virtual machine (VM) images or container images), and the configurationmanager 102 can distribute the images to host machines. In general, avirtual partition may be an instance of a VM, container, sandbox, orother isolated software environment. The software environment mayinclude an operating system and application software. For softwarerunning within a virtual partition, the virtual partition may appear tobe, for example, one of many servers or one of many operating systemsexecuted on a single physical server. The configuration manager 102 caninstantiate a new virtual partition or migrate an existing partition toa different physical server. The configuration manager 102 can also beused to configure the new or migrated sensor.

The configuration manager 102 can monitor the health of the sensors 104.For example, the configuration manager 102 may request status updatesand/or receive heartbeat messages, initiate performance tests, generatehealth checks, and perform other health monitoring tasks. In someembodiments, the configuration manager 102 can also authenticate thesensors 104. For instance, the sensors 104 can be assigned a uniqueidentifier, such as by using a one-way hash function of a sensor's basicinput/out system (BIOS) universally unique identifier (UUID) and asecret key stored by the configuration image manager 102. The UUID canbe a large number that may be difficult for a malicious sensor or otherdevice or component to guess. In some embodiments, the configurationmanager 102 can keep the sensors 104 up to date by installing the latestversions of sensor software and/or applying patches. The configurationmanager 102 can obtain these updates automatically from a local sourceor the Internet.

The sensors 104 can reside on various nodes of a network, such as avirtual partition (e.g., VM or container) 120; a hypervisor or sharedkernel managing one or more virtual partitions and/or physical servers122, an application-specific integrated circuit (ASIC) 124 of a switch,router, gateway, or other networking device, or a packet capture (pcap)126 appliance (e.g., a standalone packet monitor, a device connected toa network devices monitoring port, a device connected in series along amain trunk of a datacenter, or similar device), or other element of anetwork. The sensors 104 can monitor network traffic between nodes, andsend network traffic data and corresponding data (e.g., host data,process data, user data, etc.) to the collectors 106 for storage. Forexample, the sensors 104 can sniff packets being sent over its hosts'physical or virtual network interface card (NIC), or individualprocesses can be configured to report network traffic and correspondingdata to the sensors 104. Incorporating the sensors 104 on multiple nodesand within multiple partitions of some nodes of the network can providefor robust capture of network traffic and corresponding data from eachhop of data transmission. In some embodiments, each node of the network(e.g., VM, container, or other virtual partition 120, hypervisor, sharedkernel, or physical server 122, ASIC 124, pcap 126, etc.) includes arespective sensor 104. However, it should be understood that varioussoftware and hardware configurations can be used to implement the sensornetwork 104.

As the sensors 104 capture communications and corresponding data, theymay continuously send network traffic data to the collectors 106. Thenetwork traffic data can include metadata relating to a packet, acollection of packets, a flow, a bidirectional flow, a group of flows, asession, or a network communication of another granularity. That is, thenetwork traffic data can generally include any information describingcommunication on all layers of the Open Systems Interconnection (OSI)model. For example, the network traffic data can includesource/destination MAC address, source/destination IP address, protocol,port number, etc. In some embodiments, the network traffic data can alsoinclude summaries of network activity or other network statistics suchas number of packets, number of bytes, number of flows, bandwidth usage,response time, latency, packet loss, jitter, and other networkstatistics.

The sensors 104 can also determine additional data for each session,bidirectional flow, flow, packet, or other more granular or lessgranular network communication. The additional data can include hostand/or endpoint information, virtual partition information, sensorinformation, process information, user information, tenant information,application information, network topology, application dependencymapping, cluster information, or other information corresponding to eachflow.

In some embodiments, the sensors 104 can perform some preprocessing ofthe network traffic and corresponding data before sending the data tothe collectors 106. For example, the sensors 104 can remove extraneousor duplicative data or they can create summaries of the data (e.g.,latency, number of packets per flow, number of bytes per flow, number offlows, etc.). In some embodiments, the sensors 104 can be configured toonly capture certain types of network information and disregard therest. In some embodiments, the sensors 104 can be configured to captureonly a representative sample of packets (e.g., every 1,000th packet orother suitable sample rate) and corresponding data.

Since the sensors 104 may be located throughout the network, networktraffic and corresponding data can be collected from multiple vantagepoints or multiple perspectives in the network to provide a morecomprehensive view of network behavior. The capture of network trafficand corresponding data from multiple perspectives rather than just at asingle sensor located in the data path or in communication with acomponent in the data path, allows the data to be correlated from thevarious data sources, which may be used as additional data points by theanalytics engine 110. Further, collecting network traffic andcorresponding data from multiple points of view ensures more accuratedata is captured. For example, other types of sensor networks may belimited to sensors running on external-facing network devices (e.g.,routers, switches, network appliances, etc.) such that east-westtraffic, including VM-to-VM or container-to-container traffic on a samehost, may not be monitored. In addition, packets that are dropped beforetraversing a network device or packets containing errors may not beaccurately monitored by other types of sensor networks. The sensornetwork 104 of various embodiments substantially mitigates or eliminatesthese issues altogether by locating sensors at multiple points ofpotential failure. Moreover, the network traffic monitoring system 100can verify multiple instances of data for a flow (e.g., source endpointflow data, network device flow data, and endpoint flow data) against oneanother.

In some embodiments, the network traffic monitoring system 100 canassess a degree of accuracy of flow data sets from multiple sensors andutilize a flow data set from a single sensor determined to be the mostaccurate and/or complete. The degree of accuracy can be based on factorssuch as network topology (e.g., a sensor closer to the source may bemore likely to be more accurate than a sensor closer to thedestination), a state of a sensor or a node hosting the sensor (e.g., acompromised sensor/node may have less accurate flow data than anuncompromised sensor/node), or flow data volume (e.g., a sensorcapturing a greater number of packets for a flow may be more accuratethan a sensor capturing a smaller number of packets).

In some embodiments, the network traffic monitoring system 100 canassemble the most accurate flow data set and corresponding data frommultiple sensors. For instance, a first sensor along a data path maycapture data for a first packet of a flow but may be missing data for asecond packet of the flow while the situation is reversed for a secondsensor along the data path. The network traffic monitoring system 100can assemble data for the flow from the first packet captured by thefirst sensor and the second packet captured by the second sensor.

As discussed, the sensors 104 can send network traffic and correspondingdata to the collectors 106. In some embodiments, each sensor can beassigned to a primary collector and a secondary collector as part of ahigh availability scheme. If the primary collector fails orcommunications between the sensor and the primary collector are nototherwise possible, a sensor can send its network traffic andcorresponding data to the secondary collector. In other embodiments, thesensors 104 are not assigned specific collectors but the network trafficmonitoring system 100 can determine an optimal collector for receivingthe network traffic and corresponding data through a discovery process.In such embodiments, a sensor can change where it sends it networktraffic and corresponding data if its environments changes, such as if adefault collector fails or if the sensor is migrated to a new locationand it would be optimal for the sensor to send its data to a differentcollector. For example, it may be preferable for the sensor to send itsnetwork traffic and corresponding data on a particular path and/or to aparticular collector based on latency, shortest path, monetary cost(e.g., using private resources versus a public resources provided by apublic cloud provider), error rate, or some combination of thesefactors. In other embodiments, a sensor can send different types ofnetwork traffic and corresponding data to different collectors. Forexample, the sensor can send first network traffic and correspondingdata related to one type of process to one collector and second networktraffic and corresponding data related to another type of process toanother collector.

The collectors 106 can be any type of storage medium that can serve as arepository for the network traffic and corresponding data captured bythe sensors 104. In some embodiments, data storage for the collectors106 is located in an in-memory database, such as dashDB from IBM®,although it should be appreciated that the data storage for thecollectors 106 can be any software and/or hardware capable of providingrapid random access speeds typically used for analytics software. Invarious embodiments, the collectors 106 can utilize solid state drives,disk drives, magnetic tape drives, or a combination of the foregoingaccording to cost, responsiveness, and size requirements. Further, thecollectors 106 can utilize various database structures such as anormalized relational database or a NoSQL database, among others.

In some embodiments, the collectors 106 may only serve as networkstorage for the network traffic monitoring system 100. In suchembodiments, the network traffic monitoring system 100 can include adata mover module 108 for retrieving data from the collectors 106 andmaking the data available to network clients, such as the components ofthe analytics engine 110. In effect, the data mover module 108 can serveas a gateway for presenting network-attached storage to the networkclients. In other embodiments, the collectors 106 can perform additionalfunctions, such as organizing, summarizing, and preprocessing data. Forexample, the collectors 106 can tabulate how often packets of certainsizes or types are transmitted from different nodes of the network. Thecollectors 106 can also characterize the traffic flows going to and fromvarious nodes. In some embodiments, the collectors 106 can match packetsbased on sequence numbers, thus identifying traffic flows and connectionlinks. As it may be inefficient to retain all data indefinitely incertain circumstances, in some embodiments, the collectors 106 canperiodically replace detailed network traffic data with consolidatedsummaries. In this manner, the collectors 106 can retain a completedataset describing one period (e.g., the past minute or other suitableperiod of time), with a smaller dataset of another period (e.g., theprevious 2-10 minutes or other suitable period of time), andprogressively consolidate network traffic and corresponding data ofother periods of time (e.g., day, week, month, year, etc.). In someembodiments, network traffic and corresponding data for a set of flowsidentified as normal or routine can be winnowed at an earlier period oftime while a more complete data set may be retained for a lengthierperiod of time for another set of flows identified as anomalous or as anattack.

Computer networks may be exposed to a variety of different attacks thatexpose vulnerabilities of computer systems in order to compromise theirsecurity. Some network traffic may be associated with malicious programsor devices. The analytics engine 110 may be provided with examples ofnetwork states corresponding to an attack and network statescorresponding to normal operation. The analytics engine 110 can thenanalyze network traffic and corresponding data to recognize when thenetwork is under attack. In some embodiments, the network may operatewithin a trusted environment for a period of time so that the analyticsengine 110 can establish a baseline of normal operation. Since malwareis constantly evolving and changing, machine learning may be used todynamically update models for identifying malicious traffic patterns.

In some embodiments, the analytics engine 110 may be used to identifyobservations which differ from other examples in a dataset. For example,if a training set of example data with known outlier labels exists,supervised anomaly detection techniques may be used. Supervised anomalydetection techniques utilize data sets that have been labeled as normaland abnormal and train a classifier. In a case in which it is unknownwhether examples in the training data are outliers, unsupervised anomalytechniques may be used. Unsupervised anomaly detection techniques may beused to detect anomalies in an unlabeled test data set under theassumption that the majority of instances in the data set are normal bylooking for instances that seem to fit to the remainder of the data set.

The analytics engine 110 can include a data lake 130, an applicationdependency mapping (ADM) module 140, and elastic processing engines 150.The data lake 130 is a large-scale storage repository that providesmassive storage for various types of data, enormous processing power,and the ability to handle nearly limitless concurrent tasks or jobs. Insome embodiments, the data lake 130 is implemented using the Hadoop®Distributed File System (HDFS™) from Apache® Software Foundation ofForest Hill, Maryland. HDFS™ is a highly scalable and distributed filesystem that can scale to thousands of cluster nodes, millions of files,and petabytes of data. HDFS™ is optimized for batch processing wheredata locations are exposed to allow computations to take place where thedata resides. HDFS™ provides a single namespace for an entire cluster toallow for data coherency in a write-once, read-many access model. Thatis, clients can only append to existing files in the node. In HDFS™,files are separated into blocks, which are typically 64 MB in size andare replicated in multiple data nodes. Clients access data directly fromdata nodes.

In some embodiments, the data mover 108 receives raw network traffic andcorresponding data from the collectors 106 and distributes or pushes thedata to the data lake 130. The data lake 130 can also receive and storeout-of-band data 114, such as statuses on power levels, networkavailability, server performance, temperature conditions, cage doorpositions, and other data from internal sources, and third party data116, such as security reports (e.g., provided by Cisco® Systems, Inc. ofSan Jose, Calif., Arbor Networks® of Burlington, Mass., Symantec® Corp.of Sunnyvale, Calif., Sophos® Group plc of Abingdon, England, Microsoft®Corp. of Seattle, Wash., Verizon® Communications, Inc. of New York,N.Y., among others), geolocation data, IP watch lists, Whois data,configuration management database (CMDB) or configuration managementsystem (CMS) as a service, and other data from external sources. Inother embodiments, the data lake 130 may instead fetch or pull rawtraffic and corresponding data from the collectors 106 and relevant datafrom the out-of-band data sources 114 and the third party data sources116. In yet other embodiments, the functionality of the collectors 106,the data mover 108, the out-of-band data sources 114, the third partydata sources 116, and the data lake 130 can be combined. Variouscombinations and configurations are possible as would be known to one ofordinary skill in the art.

Each component of the data lake 130 can perform certain processing ofthe raw network traffic data and/or other data (e.g., host data, processdata, user data, out-of-band data or third party data) to transform theraw data to a form useable by the elastic processing engines 150. Insome embodiments, the data lake 130 can include repositories for flowattributes 132, host and/or endpoint attributes 134, process attributes136, and policy attributes 138. In some embodiments, the data lake 130can also include repositories for VM or container attributes,application attributes, tenant attributes, network topology, applicationdependency maps, cluster attributes, etc.

The flow attributes 132 relate to information about flows traversing thenetwork. A flow is generally one or more packets sharing certainattributes that are sent within a network within a specified period oftime. The flow attributes 132 can include packet header fields such as asource address (e.g., Internet Protocol (IP) address, Media AccessControl (MAC) address, Domain Name System (DNS) name, or other networkaddress), source port, destination address, destination port, protocoltype, class of service, among other fields. The source address maycorrespond to a first endpoint (e.g., network device, physical server,virtual partition, etc.) of the network, and the destination address maycorrespond to a second endpoint, a multicast group, or a broadcastdomain. The flow attributes 132 can also include aggregate packet datasuch as flow start time, flow end time, number of packets for a flow,number of bytes for a flow, the union of TCP flags for a flow, amongother flow data.

The host and/or endpoint attributes 134 describe host and/or endpointdata for each flow, and can include host and/or endpoint name, networkaddress, operating system, CPU usage, network usage, disk space, ports,logged users, scheduled jobs, open files, and information regardingfiles and/or directories stored on a host and/or endpoint (e.g.,presence, absence, or modifications of log files, configuration files,device special files, or protected electronic information). Asdiscussed, in some embodiments, the host and/or endpoints attributes 134can also include the out-of-band data 114 regarding hosts such as powerlevel, temperature, and physical location (e.g., room, row, rack, cagedoor position, etc.) or the third party data 116 such as whether a hostand/or endpoint is on an IP watch list or otherwise associated with asecurity threat, Whois data, or geo-coordinates. In some embodiments,the out-of-band data 114 and the third party data 116 may be associatedby process, user, flow, or other more granular or less granular networkelement or network communication.

The process attributes 136 relate to process data corresponding to eachflow, and can include process name (e.g., bash, httpd, netstat, etc.),ID, parent process ID, path (e.g., /usr2/username/bin/, /usr/local/bin,/usr/bin, etc.), CPU utilization, memory utilization, memory address,scheduling information, nice value, flags, priority, status, start time,terminal type, CPU time taken by the process, the command that startedthe process, and information regarding a process owner (e.g., user name,ID, user's real name, e-mail address, user's groups, terminalinformation, login time, expiration date of login, idle time, andinformation regarding files and/or directories of the user).

The policy attributes 138 contain information relating to networkpolicies. Policies establish whether a particular flow is allowed ordenied by the network as well as a specific route by which a packettraverses the network. Policies can also be used to mark packets so thatcertain kinds of traffic receive differentiated service when used incombination with queuing techniques such as those based on priority,fairness, weighted fairness, token bucket, random early detection, roundrobin, among others. The policy attributes 138 can include policystatistics such as a number of times a policy was enforced or a numberof times a policy was not enforced. The policy attributes 138 can alsoinclude associations with network traffic data. For example, flows foundto be non-conformant can be linked or tagged with corresponding policiesto assist in the investigation of non-conformance.

The analytics engine 110 may include any number of engines 150,including for example, a flow engine 152 for identifying flows (e.g.,flow engine 152) or an attacks engine 154 for identify attacks to thenetwork. In some embodiments, the analytics engine can include aseparate distributed denial of service (DDoS) attack engine 155 forspecifically detecting DDoS attacks. In other embodiments, a DDoS attackengine may be a component or a sub-engine of a general attacks engine.In some embodiments, the attacks engine 154 and/or the DDoS engine 155can use machine learning techniques to identify security threats to anetwork. For example, the attacks engine 154 and/or the DDoS engine 155can be provided with examples of network states corresponding to anattack and network states corresponding to normal operation. The attacksengine 154 and/or the DDoS engine 155 can then analyze network trafficdata to recognize when the network is under attack. In some embodiments,the network can operate within a trusted environment for a time toestablish a baseline for normal network operation for the attacks engine154 and/or the DDoS.

The analytics engine 110 may further include a search engine 156. Thesearch engine 156 may be configured, for example to perform a structuredsearch, an NLP (Natural Language Processing) search, or a visual search.Data may be provided to the engines from one or more processingcomponents.

The analytics engine 110 can also include a policy engine 158 thatmanages network policy, including creating and/or importing policies,monitoring policy conformance and non-conformance, enforcing policy,simulating changes to policy or network elements affecting policy, amongother policy-related tasks.

The ADM module 140 can determine dependencies of applications of thenetwork. That is, particular patterns of traffic may correspond to anapplication, and the interconnectivity or dependencies of theapplication can be mapped to generate a graph for the application (i.e.,an application dependency mapping). In this context, an applicationrefers to a set of networking components that provides connectivity fora given set of workloads. For example, in a three-tier architecture fora web application, first endpoints of the web tier, second endpoints ofthe application tier, and third endpoints of the data tier make up theweb application. The ADM module 140 can receive input data from variousrepositories of the data lake 130 (e.g., the flow attributes 132, thehost and/or endpoint attributes 134, the process attributes 136, etc.).The ADM module 140 may analyze the input data to determine that there isfirst traffic flowing between external endpoints on port 80 of the firstendpoints corresponding to Hypertext Transfer Protocol (HTTP) requestsand responses. The input data may also indicate second traffic betweenfirst ports of the first endpoints and second ports of the secondendpoints corresponding to application server requests and responses andthird traffic flowing between third ports of the second endpoints andfourth ports of the third endpoints corresponding to database requestsand responses. The ADM module 140 may define an ADM for the webapplication as a three-tier application including a first EPG comprisingthe first endpoints, a second EPG comprising the second endpoints, and athird EPG comprising the third endpoints.

The presentation module 112 can include an application programminginterface (API) or command line interface (CLI) 160, a securityinformation and event management (SIEM) interface 162, and a webfront-end 164. As the analytics engine 110 processes network traffic andcorresponding data and generates analytics data, the analytics data maynot be in a human-readable form or it may be too voluminous for a userto navigate. The presentation module 112 can take the analytics datagenerated by analytics engine 110 and further summarize, filter, andorganize the analytics data as well as create intuitive presentationsfor the analytics data.

In some embodiments, the API or CLI 160 can be implemented using Hadoop®Hive from Apache® for the back end, and Java® Database Connectivity(JDBC) from Oracle® Corporation of Redwood Shores, Calif., as an APIlayer. Hive is a data warehouse infrastructure that provides datasummarization and ad hoc querying. Hive provides a mechanism to querydata using a variation of structured query language (SQL) that is calledHiveQL. JDBC is an application programming interface (API) for theprogramming language Java®, which defines how a client may access adatabase.

In some embodiments, the SIEM interface 162 can be implemented usingKafka for the back end, and software provided by Splunk®, Inc. of SanFrancisco, Calif. as the SIEM platform. Kafka is a distributed messagingsystem that is partitioned and replicated. Kafka uses the concept oftopics. Topics are feeds of messages in specific categories. In someembodiments, Kafka can take raw packet captures and telemetryinformation from the data mover 108 as input, and output messages to aSIEM platform, such as Splunk®. The Splunk® platform is utilized forsearching, monitoring, and analyzing machine-generated data.

In some embodiments, the web front-end 164 can be implemented usingsoftware provided by MongoDB®, Inc. of New York, N.Y. and Hadoop®ElasticSearch from Apache® for the back-end, and Ruby on Rails™ as theweb application framework. MongoDB® is a document-oriented NoSQLdatabase based on documents in the form of JavaScript® Object Notation(JSON) with dynamic schemas. ElasticSearch is a scalable and real-timesearch and analytics engine that provides domain-specific language (DSL)full querying based on JSON. Ruby on Rails™ is model-view-controller(MVC) framework that provides default structures for a database, a webservice, and web pages. Ruby on Rails™ relies on web standards such asJSON or extensible markup language (XML) for data transfer, andhypertext markup language (HTML), cascading style sheets, (CSS), andJavaScript® for display and user interfacing.

Although FIG. 1 illustrates an example configuration of the variouscomponents of a network traffic monitoring system, those of skill in theart will understand that the components of the network trafficmonitoring system 100 or any system described herein can be configuredin a number of different ways and can include any other type and numberof components. For example, the sensors 104, the collectors 106, thedata mover 108, and the data lake 130 can belong to one hardware and/orsoftware module or multiple separate modules. Other modules can also becombined into fewer components and/or further divided into morecomponents.

FIG. 2 illustrates an example of a network environment, according to oneaspect of the present disclosure.

In some embodiments, a network traffic monitoring system, such as thenetwork traffic monitoring system 100 of FIG. 1 , can be implemented inthe network environment 200. It should be understood that, for thenetwork environment 200 and any environment discussed herein, there canbe additional or fewer nodes, devices, links, networks, or components insimilar or alternative configurations. Embodiments with differentnumbers and/or types of clients, networks, nodes, cloud components,servers, software components, devices, virtual or physical resources,configurations, topologies, services, appliances, deployments, ornetwork devices are also contemplated herein. Further, the networkenvironment 200 can include any number or type of resources, which canbe accessed and utilized by clients or tenants. The illustrations andexamples provided herein are for clarity and simplicity.

The network environment 200 can include a network fabric 202, a Layer 2(L2) network 204, a Layer 3 (L3) network 206, and servers 208 a, 208 b,208 c, 208 d, and 208 e (collectively, 208). The network fabric 202 caninclude spine switches 210 a, 210 b, 210 c, and 210 d (collectively,“210”) and leaf switches 212 a, 212 b, 212 c, 212 d, and 212 e(collectively, “212”). The spine switches 210 can connect to the leafswitches 212 in the network fabric 202. The leaf switches 212 caninclude access ports (or non-fabric ports) and fabric ports. The fabricports can provide uplinks to the spine switches 210, while the accessports can provide connectivity to endpoints (e.g., the servers 208),internal networks (e.g., the L2 network 204), or external networks(e.g., the L3 network 206).

The leaf switches 212 can reside at the edge of the network fabric 202,and can thus represent the physical network edge. For instance, in someembodiments, the leaf switches 212 d and 212 e operate as border leafswitches in communication with edge devices 214 located in the externalnetwork 206. The border leaf switches 212 d and 212 e may be used toconnect any type of external network device, service (e.g., firewall,deep packet inspector, traffic monitor, load balancer, etc.), or network(e.g., the L3 network 206) to the fabric 202.

Although the network fabric 202 is illustrated and described herein asan example leaf-spine architecture, one of ordinary skill in the artwill readily recognize that various embodiments can be implemented basedon any network topology, including any data center or cloud networkfabric. Indeed, other architectures, designs, infrastructures, andvariations are contemplated herein. For example, the principlesdisclosed herein are applicable to topologies including three-tier(including core, aggregation, and access levels), fat tree, mesh, bus,hub and spoke, etc. Thus, in some embodiments, the leaf switches 212 canbe top-of-rack switches configured according to a top-of-rackarchitecture. In other embodiments, the leaf switches 212 can beaggregation switches in any particular topology, such as end-of-row ormiddle-of-row topologies. In some embodiments, the leaf switches 212 canalso be implemented using aggregation switches.

Moreover, the topology illustrated in FIG. 2 and described herein isreadily scalable and may accommodate a large number of components, aswell as more complicated arrangements and configurations. For example,the network may include any number of fabrics 202, which may begeographically dispersed or located in the same geographic area. Thus,network nodes may be used in any suitable network topology, which mayinclude any number of servers, virtual machines or containers, switches,routers, appliances, controllers, gateways, or other nodesinterconnected to form a large and complex network. Nodes may be coupledto other nodes or networks through one or more interfaces employing anysuitable wired or wireless connection, which provides a viable pathwayfor electronic communications.

Network communications in the network fabric 202 can flow through theleaf switches 212. In some embodiments, the leaf switches 212 canprovide endpoints (e.g., the servers 208), internal networks (e.g., theL2 network 204), or external networks (e.g., the L3 network 206) accessto the network fabric 202, and can connect the leaf switches 212 to eachother. In some embodiments, the leaf switches 212 can connect endpointgroups (EPGs) to the network fabric 202, internal networks (e.g., the L2network 204), and/or any external networks (e.g., the L3 network 206).EPGs are groupings of applications, or application components, and tiersfor implementing forwarding and policy logic. EPGs can allow forseparation of network policy, security, and forwarding from addressingby using logical application boundaries. EPGs can be used in the networkenvironment 200 for mapping applications in the network. For example,EPGs can comprise a grouping of endpoints in the network indicatingconnectivity and policy for applications.

As discussed, the servers 208 can connect to the network fabric 202 viathe leaf switches 212. For example, the servers 208 a and 208 b canconnect directly to the leaf switches 212 a and 212 b, which can connectthe servers 208 a and 208 b to the network fabric 202 and/or any of theother leaf switches. The servers 208 c and 208 d can connect to the leafswitches 212 b and 212 c via the L2 network 204. The servers 208 c and208 d and the L2 network 204 make up a local area network (LAN). LANscan connect nodes over dedicated private communications links located inthe same general physical location, such as a building or campus.

The WAN 206 can connect to the leaf switches 212 d or 212 e via the L3network 206. WANs can connect geographically dispersed nodes overlong-distance communications links, such as common carrier telephonelines, optical light paths, synchronous optical networks (SONET), orsynchronous digital hierarchy (SDH) links. LANs and WANs can include L2and/or L3 networks and endpoints.

The Internet is an example of a WAN that connects disparate networksthroughout the world, providing global communication between nodes onvarious networks. The nodes typically communicate over the network byexchanging discrete frames or packets of data according to predefinedprotocols, such as the Transmission Control Protocol/Internet Protocol(TCP/IP). In this context, a protocol can refer to a set of rulesdefining how the nodes interact with each other. Computer networks maybe further interconnected by an intermediate network node, such as arouter, to extend the effective size of each network. The endpoints 208can include any communication device or component, such as a computer,server, blade, hypervisor, virtual machine, container, process (e.g.,running on a virtual machine), switch, router, gateway, host, device,external network, etc.

In some embodiments, the network environment 200 also includes a networkcontroller running on the host 208 a. The network controller isimplemented using the Application Policy Infrastructure Controller(APIC™) from Cisco®. The APIC™ provides a centralized point ofautomation and management, policy programming, application deployment,and health monitoring for the fabric 202. In some embodiments, the APIC™is operated as a replicated synchronized clustered controller. In otherembodiments, other configurations or software-defined networking (SDN)platforms can be utilized for managing the fabric 202.

In some embodiments, a physical server 208 may have instantiated thereona hypervisor 216 for creating and running one or more virtual switches(not shown) and one or more virtual machines 218, as shown for the host208 b. In other embodiments, physical servers may run a shared kernelfor hosting containers. In yet other embodiments, the physical server208 can run other software for supporting other virtual partitioningapproaches. Networks in accordance with various embodiments may includeany number of physical servers hosting any number of virtual machines,containers, or other virtual partitions. Hosts may also compriseblade/physical servers without virtual machines, containers, or othervirtual partitions, such as the servers 208 a, 208 c, 208 d, and 208 e.

The network environment 200 can also integrate a network trafficmonitoring system, such as the network traffic monitoring system 100shown in FIG. 1 . For example, the network traffic monitoring system ofFIG. 2 includes sensors 220 a, 220 b, 220 c, and 220 d (collectively,“220”), collectors 222, and an analytics engine, such as the analyticsengine 110 of FIG. 1 , executing on the server 208 e. The analyticsengine 208 e can receive and process network traffic data collected bythe collectors 222 and detected by the sensors 220 placed on nodeslocated throughout the network environment 200. Although the analyticsengine 208 e is shown to be a standalone network appliance in FIG. 2 ,it will be appreciated that the analytics engine 208 e can also beimplemented as a virtual partition (e.g., VM or container) that can bedistributed onto a host or cluster of hosts, software as a service(SaaS), or other suitable method of distribution. In some embodiments,the sensors 220 run on the leaf switches 212 (e.g., the sensor 220 a),the hosts 208 (e.g., the sensor 220 b), the hypervisor 216 (e.g., thesensor 220 c), and the VMs 218 (e.g., the sensor 220 d). In otherembodiments, the sensors 220 can also run on the spine switches 210,virtual switches, service appliances (e.g., firewall, deep packetinspector, traffic monitor, load balancer, etc.) and in between networkelements. In some embodiments, sensors 220 can be located at each (ornearly every) network component to capture granular packet statisticsand data at each hop of data transmission. In other embodiments, thesensors 220 may not be installed in all components or portions of thenetwork (e.g., shared hosting environment in which customers haveexclusive control of some virtual machines).

As shown in FIG. 2 , a host may include multiple sensors 220 running onthe host (e.g., the host sensor 220 b) and various components of thehost (e.g., the hypervisor sensor 220 c and the VM sensor 220 d) so thatall (or substantially all) packets traversing the network environment200 may be monitored. For example, if one of the VMs 218 running on thehost 208 b receives a first packet from the WAN 206, the first packetmay pass through the border leaf switch 212 d, the spine switch 210 b,the leaf switch 212 b, the host 208 b, the hypervisor 216, and the VM.Since all or nearly all of these components contain a respective sensor,the first packet will likely be identified and reported to one of thecollectors 222. As another example, if a second packet is transmittedfrom one of the VMs 218 running on the host 208 b to the host 208 d,sensors installed along the data path, such as at the VM 218, thehypervisor 216, the host 208 b, the leaf switch 212 b, and the host 208d will likely result in capture of metadata from the second packet.

FIG. 3 illustrates an example of a data pipeline for generating networkinsights based on collected network information, according to one aspectof the present disclosure.

The insights generated from data pipeline 300 may include, for example,discovered applications or inventories, application dependencies,policies, efficiencies, resource and bandwidth usage, network flows andstatus of devices and/or associated users having access to the networkcan be determined for the network using the network traffic data. Insome embodiments, the data pipeline 300 can be directed by a networktraffic monitoring system, such as the network traffic monitoring system100 of FIG. 1 ; an analytics engine, such as the analytics engine 110 ofFIG. 1 ; or other network service or network appliance. For example, ananalytics engine 110 can be configured to discover applications runningin the network, map the applications' interdependencies, generate a setof proposed network policies for implementation, and monitor policyconformance and non-conformance among other network-related tasks.

The data pipeline 300 includes a data collection stage 302 in whichnetwork traffic data and corresponding data (e.g., host data, processdata, user data, etc.) are captured by sensors (e.g., the sensors 104 ofFIG. 1 ) located throughout the network. The data may comprise, forexample, raw flow data and raw process data. As discussed, the data canbe captured from multiple perspectives to provide a comprehensive viewof the network. The data collected may also include other types ofinformation, such as tenant information, virtual partition information,out-of-band information, third party information, and other relevantinformation. In some embodiments, the flow data and associated data canbe aggregated and summarized daily or according to another suitableincrement of time, and flow vectors, process vectors, host vectors, andother feature vectors can be calculated during the data collection stage302. This can substantially reduce processing.

The data pipeline 300 may also include an input data stage 304 in whicha network or security administrator or other authorized user mayconfigure insight generation by selecting the date range of the flowdata and associated data to analyze, and those nodes for which theadministrator wants to analyze. In some embodiments, the administratorcan also input side information, such as server load balance, routetags, and previously identified clusters during the input data stage304. In other embodiments, the side information can be automaticallypulled or another network element can push the side information.

The next stage of the data pipeline 300 is pre-processing 306. Duringthe pre-processing stage 306, nodes of the network are partitioned intoselected node and dependency node subnets. Selected nodes are thosenodes for which the user requests application dependency maps andcluster information. Dependency nodes are those nodes that are notexplicitly selected by the users for an ADM run but are nodes thatcommunicate with the selected nodes. To obtain the partitioninginformation, edges of an application dependency map (i.e., flow data)and unprocessed feature vectors can be analyzed.

Other tasks can also be performed during the pre-processing stage 306,including identifying dependencies of the selected nodes and thedependency nodes; replacing the dependency nodes with tags based on thedependency nodes' subnet names; extracting feature vectors for theselected nodes, such as by aggregating daily vectors across multipledays, calculating term frequency-inverse document frequency (tf-idf),and normalizing the vectors (e.g.,

normalization); and identifying existing clusters.

In some embodiments, the pre-processing stage 306 can include earlyfeature fusion pre-processing. Early fusion is a fusion scheme in whichfeatures are combined into a single representation. Features may bederived from various domains (e.g., network, host, virtual partition,process, user, etc.), and a feature vector in an early fusion system mayrepresent the concatenation of disparate feature types or domains.

Early fusion may be effective for features that are similar or have asimilar structure (e.g., fields of TCP and UDP packets or flows). Suchfeatures may be characterized as being a same type or being within asame domain. Early fusion may be less effective for distant features orfeatures of different types or domains (e.g., flow-based features versusprocess-based features). Thus, in some embodiments, only features in thenetwork domain (i.e., network traffic-based features, such as packetheader information, number of packets for a flow, number of bytes for aflow, and similar data) may be analyzed. In other embodiments, analysismay be limited to features in the process domain (i.e., process-basedfeatures, such as process name, parent process, process owner, etc.). Inyet other embodiments, feature sets in other domains (e.g., the hostdomain, virtual partition domain, user domain, etc.) may be the.

After pre-processing, the data pipeline 300 may proceed to an insightgeneration stage 308. During the insight generation stage 308, the datacollected and inputted into the data pipeline 300 may be used togenerate various network insights. For example, an analytics engine 110can be configured to discover of applications running in the network,map the applications' interdependencies, generate a set of proposednetwork policies for implementation, and monitor policy conformance andnon-conformance among other network-related tasks. Various machinelearning techniques can be implemented to analyze feature vectors withina single domain or across different domains to generate insights.Machine learning is an area of computer science in which the goal is todevelop models using example observations (i.e., training data), thatcan be used to make predictions on new observations. The models or logicare not based on theory but are empirically based or data-driven.

After clusters are identified, the data pipeline 300 can include apost-processing stage 310. The post-processing stage 310 can includetasks such as filtering insight data, converting the insight data into aconsumable format, or any other preparations needed to prepare theinsight data for consumption by an end user. At the output stage 312,the generated insights may be provided to an end user. The end user maybe, for example a network administrator, a third-party computing system,a computing system in the network, or any other entity configured toreceive the insight data. In some cases, the insight data may beconfigured to be displayed on a screen or provided to a system forfurther processing, consumption, or storage.

As noted above, there is a need to improve security of networks such asnetwork environment 200 of FIG. 2 with hundreds to thousands ofendpoints and workloads being in continuous communication with oneanother and/or with sources external to the network. Such externalcommunication can be provide an opportunity for unauthorized access tosuch networks. Accordingly, one example approach for improving thesecurity of networks is by detecting compromised workloads and endpointsand preventing partial or complete access to or from the compromisedworkloads and endpoints, by other nodes in the network. As will bedescribed below, the partial or complete prevention of access tocompromised workloads and endpoints may be achieved via blocking accessto such compromised workloads and endpoints and/or quarantining affectedendpoints. Detection of compromised workloads and endpoints may be basedon a list of known threats (e.g., known Cyber Threat Alliance (CTA)threats) that identify malicious sources, Universal Resource Locators(URLs), etc. Such list may be distributed to agents deployed on networknodes that are configured to monitoring network traffic andcorresponding sources and destinations.

With examples of network traffic monitoring systems, their operationsand network environments in which they can be deployed described above,the disclosure now turns to FIGS. 4 and 5 , which describe examples ofsettings in which network elements may be exposed to malicious threats.

FIG. 4 illustrates a simplified version of a setting in which elementsof network environment of FIG. 2 communicate with potential networkthreats, according to one aspect of the present disclosure.

Setting 400 can include a networking environment 401 that can be thesame as networking environment 200 of FIG. 2 (e.g., a datacenter, anenterprise network, etc.). Network environment 401 can include a numberof element such as server 402 accessible via cloud 404. Among otherknown or to be developed functionalities, server 402 may communicatewith one or more network nodes and workloads such as element 401-1,element 406-2 and element 406-3 (collectively referred to as networkelements 406). Any given one of network elements 406 can be a networknode including, but not limited to, a physical host, a physical server,a network device such as a router, a switch, a virtual server, ahypervisor or shared kernels, a virtual partition (e.g., VMs orcontainers), etc. Furthermore, network elements 406 can be workloads orsegments of a network-wide workload/applications being executed ondifferent hosts or network nodes of setting 400. For example, networkelements 406 may corresponding to a Human Resources procurement softwareutilized by an organization associated with network environment 401, abilling system for such organization, etc.

Each network element 406 may have a corresponding sensor installedthereon. For example, network element 406-1 can have sensor 408-1installed thereon, network element 406-2 can have sensor 408-2 installedthereon, network element 406-3 can have sensor 408-3 installed thereon,etc. Sensors 408-1, 408-2 and 408-3 may collectively be referred to assensors 408. Sensors 408 may be the same as sensor 104. Among otherfunctionalities, such as those described above with reference to FIG. 1, sensors 408 may monitor various statistics associated with operationsof network elements 406 including, but not limited to, sources anddestinations of network traffic to and from network elements 406, etc.Furthermore, sensors 408 may receive updates from server 402 regardingnetwork threats (e.g., malicious IP addresses, categories of threats,malware, etc.). This will be further described below. In cases where anetwork element 406 is a software package/workload, corresponding sensor408 may also be a software package installed and executed as part ofsuch workload.

Communications between server 402 and sensors 408 may be via links 410while communications between network elements 406 may be via links 412.Links 410 and 412 may be any known or to be developed wired and/orwireless communication link enabling uni-directional and/orbi-directional communication connected points thereto.

While in example network environment 401 only three network elements 406are shown, the present disclosure is not limited thereto and networkenvironment 401 may include many more such network elements 406 (e.g. inthe order of tens, hundreds, thousands and/or hundreds of thousands ofnetwork elements). Furthermore, while network environment 401illustrates each network element 406 with a dedicated sensor 408, thepresent disclosure is not limited thereto. For example, two or morenetwork elements 406 may share a sensor 408 or alternatively, a givennetwork element may have more than one sensor 408 installed thereon.

In example setting 400, network elements 406 may communicate with one ormore hosts such as hosts 414-1 and 414-2 (collectively referred to ashosts 414). Hosts 414 may be external to network environment 401 (asshown) or can be internal to network environment 401. For example, hosts414 can be external servers accessed by one or more network elements406. In example of FIG. 4 , network element 406-3 is shown to becommunicating with hosts 414 via links 416, which similar to links 410and 412 can be any known or to be developed wired and/or wirelesscommunication link enabling uni-directional and/or bi-directionalcommunication connected points thereto. However, the disclosure is notlimited thereto and any one of network elements 406 may communicatewith/access any one of hosts 414. Furthermore, while FIG. 4 illustratestwo hosts 414, the present disclosure is not limited thereto and setting400 can have any number of hosts 414 with which one or more networkelements 406 communicate.

At any given point in time, one or more of hosts 414 may be associatedwith a network threat that can be a malicious (unauthorized) software, amalware, a known malicious IP address, a source associated with acategory of threats, etc. In one example, one or more of hosts 414 mayhave malicious software running thereon or may have attempted to accessa malicious software or host. Such host 414 may be referred to as acompromised host (compromised element). Network environment 401 includesa list 418 (which will be further described below). List 418 may be alist of known threats (e.g., CTA threats described above) that may beperiodically obtained by (provided to) server 402. List 418 may begenerated using a database of threats via crowdsourcing, etc. Furtherdetails regarding list 418 and its availability to server 402 will bedescribed below.

Server 402 may communicate the updated list of known threats to sensors408. Upon detecting access to compromised hosts by any one of networkelements 406, as will be described below, sensors 408 may prevent accessto such compromised hosts by network elements 406. For example, insetting 400, host 414-1 may contain a malicious software. Accordingly,host 414-1 may be considered a compromised host. Sensor 408-3 running onnetwork element 406-3 may detect access to compromised host 414-1. Aswill be described below, since sensor 408-3 is aware of the malicioussoftware or malicious nature of host 414-1 (based on the updated listprovided to sensor 408-3 by server 402), sensor 408-3 may cause networkelement 406-3 to block (break) communication link 416 to and fromcompromised host 414-1. Such break of communication link 416 may beconfined to just compromised host 414-1 and network element 406-3, mayinclude preventing any direct or indirect communication betweencompromised host 414-1 and any other network element 406 or server 402in setting 400, etc. Alternatively, compromised host 414-1 may bequarantined (e.g., temporarily) from accessing any resources on setting400 until the threat associated with compromised host 414-1 is addressedby server 402. This will be further described below. Different types ofaccess prevention will be further described below.

FIG. 5 illustrates another simplified version of a setting in whichelements of network environment of FIG. 2 communicate with potentialmalicious hosts, according to one aspect of the present disclosure.

Similar to setting 400, setting 500 may include a network environment501 that can be the same as network environment 401 and thereforeelements there that are the same as their counterparts in networkenvironment 401 are numbered the same and thus will not be describedfurther for sake of brevity. For example, server 402 of networkenvironment 501 is the same as server 402 of network environment 401 ofFIG. 4 , network elements 406 of network environment 501 are the same asnetwork elements 406 of network environment 401 of FIG. 4 , etc.

In contrast to network environment 401, network environment 501 includesone or more endpoint 1 520-1 and endpoint 2 520-2 (collectively referredto as endpoints 520). Endpoints 520 can be any known or to be developeddevice capable of establishing a communication with one or more elementsof network environment 501. For example, endpoints 520 can be any one ofa mobile phone, a laptop, a tablet, a desktop, an Internet of Things(IoT) device, etc. Endpoints 520 can be registered with networkenvironment 501 according to any known or to be developed method.Endpoints 520 can be physically located in the same location as one inwhich network environment 501 is deployed or can be remote andcommunicatively coupled to one or more nodes or elements of networkenvironment 501. For example, endpoints 520 can have remote connectionagents (e.g., ANYCONNECT developed by Cisco Inc. of San Jose, Calif.)installed thereon, which upon activation requires corresponding users toprovide credentials through the installed agents. Upon authentication,such remote connection agents enable endpoints 520 to remotely accessresources of network environment 501.

Endpoints 520 can communicate (e.g., for accessing a workload) with oneor more network elements 502. For example, endpoint 520-1 cancommunicate with network elements 506-2 and 506-3 via communicationlinks 522 (communication links 522 can be the same as communicationlinks 410/412 of FIG. 4 and thus will not be described further).Furthermore, endpoint 520-1 can communicate with network elements 406-1and 406-3 via communication links 524 (communication links 524 can bethe same as communication links 410/412 of FIG. 4 and thus will not bedescribed further).

In setting 500, one or more of endpoints 520 may communicate with anexternal source such as host 526. For example, while connected tonetwork environment 501 and accessing a workload on network elements406-2 and 406-3 (e.g., a billing system of an organization associatedwith network environment 501), a user of endpoint 520-1 may accessexternal host 526 via a website and communication link 528(communication links 528 can be the same as communication links 410/412of FIG. 4 and thus will not be described further). Host 526 may have oneor more known threats associated therewith such as a malware.Communication with host 526 may result in endpoint 520-1 beingconsidered a compromised endpoint (compromised element). Accordingly,sensors 408-2 and 408-3 running network elements 406-2 and 406-3 maycompare access to host 526 by endpoint 520-1 to list 418 and determinethat host 526 is a malicious host (a bad host). Accordingly, endpoint520-1 may be blocked/quarantined so that no communication betweenelements of network environment 501 and endpoint 520-1 can beestablished. Such quarantine can be temporary (e.g., for a period oftime such as an hour, a day, a week, etc.) or can be permanent (e.g.,requiring endpoint 520-1 to be rebooted with original settings, etc.).Different types of access prevention will be further described below.

Next, examples for setting up a process to identify the network threatsand implement a dynamic access prevention scheme when threats areidentified will be described with reference to FIGS. 6 and 7 .

FIG. 6 describes a process for processing and fetching a list of knownnetwork threats to network sensors for detecting compromised workloadsand endpoints, according to one aspect of the present disclosure.

Process of FIG. 6 will be described from the perspective of server 402of FIG. 4 . However, it should be understood that server 402 may haveone or more processors associated therewith that are configured toexecute computer-readable instructions to perform the steps of FIG. 6 .

At S600, server 402 receives a list of network threats (or simplythreats). A list of threats can be a list of known threats (e.g.,malicious IP addresses, malwares, etc.) developed by CTA and may bereferred to as a CTA list. A CTA or any other list of known threats maybe developed using crowdsourcing, etc. and may be publicly available.Such list may be received (queried by server 402) from any known sourcesuch as CTA Amazon Web Services (AWS) Lambda bucket or any other type ofsource containing such list. In one example, the list of threats may becommunicated to server 402 periodically (e.g., once a day, once a week,once a month, etc.) or may be queried by server 402 periodically.

At S602, server 402 transforms the list of network threats. Thetransformation process may be an optional step and performed to ensurethat the format of the data packets containing the threat listinformation are compatible with existing exchange protocols betweenserver 402 and sensors 408.

At S604, server 402 may store the transformed list of network threats ina database or a storage associated with server 402 (not shown in FIGS. 4and 5 ).

At S606, the transformed list of network threats (threat feeds) arepackaged into existing data packs exchanged between server 402 andsensors 408. Data packs may include an auxiliary information portion inwhich threat feeds can be included. Data packs are provided to server402 and sensors 408 via known external resources. For example, datapacks can originate from a cloud headend hosting one or more data jobsin association with which data packs are created and send to server 402.

In one example, packaging of threat feeds into data packs may be donevia a feature called a cloud connection. This can be an AWS service thatsensors 408 can connect to, via server 402 for example, to fetch thelatest data packs. In response to this, sensors 408 can also send backusage statistics about various features back to server 402. In anotherexample, data packets can be packaged into Red Hat Packet Managers(RPMs) that can be downloaded from a website, and uploaded to sensors408. In one example, an RPM is a mechanism in which data packs arepackaged and sent as one unit from cloud headend to server 402. An RPMcan be arbitrary set of files that can be sent and dealt on a systemsuch as a UNIX system.

At S608, server 402 can define policies for the threat feeds by creatingannotations, which can then be used to tag/identify compromisedworkloads/endpoints. Such annotation may be a tag identifying a knownmalicious (bad) IP address on the received list or can be a tagidentifying a threat category and source(s) of such category.

For example, a policy can be defined to identify workloads/endpointsthat should block connections to a malicious IP address. In such case,as data packets come in to such workloads/endpoints (e.g., networkelement 406-3 of FIG. 4 ), the source of the data packet is examined bythe corresponding sensor 408 and if the source matches the knownmalicious IP (e.g., associated with host 414-1), network element 406-3′sconnection to host 414-1 is taken down/blocked.

At S610, server 402 deploys defined policies and threat feeds to sensors408 for implementation and prevent access to network resources andelements by compromised workloads/endpoints. This will be furtherdescribed below reference to FIG. 7

At S612, server 402 may receive continuous feedback from sensors 408 onvarious statistics collected by sensors 408 on performance and datatraffic between their respective network elements 406 and/or otherendpoints and hosts inside or external to network environment 401/501.Such statistics may be used by server 402 to further refine/updateannotations and policies created for preventing access to networkresources by malicious sources and known threats. For example, whensensors 408 detect occurrence of a malware across ten network elements(that can potentially span different across customers), based on suchincreased occurrence, server 402 can define policies on other networkelements to prevent to any host or endpoint on which the malware isdetected through a policy action or quarantine any endpoint on which themalware is detected.

FIG. 7 is an example of network monitoring process for preventingnetwork access from malicious sources, according to one aspect of thepresent disclosure.

FIG. 7 will be described from the perspective of server 402 of FIG. 4 .However, it should be understood that server 402 may implement theprocesses of FIG. 7 using one or more sensors 408.

At S700, using sensors 408, server 402 monitors network traffic betweennetwork elements 406 of network environment 401 and one or moreendpoints and/or hosts such as hosts 414 and/or endpoints 520.

At S702, using tags associated with defined policies per process of FIG.6 , server 402 identifies a network threat (e.g., a malicious IPaddress, a malware, etc.) in association with any one of hosts 414and/or endpoints 520. Such identification may be based on defined bad IPaddresses and/or tags associated with malicious categories of malwares,viruses, bugs, known cyber threats, etc., and corresponding resources,as described above with reference to FIG. 6 . Host(s) 414, endpoint(s)520 and/or associated workloads in connection with which maliciousaddress/malware is/are detected, may be referred to as compromisedelements.

At S704, server 402 detects compromised element(s) (e.g., workload(s),endpoint(s), etc.) based on identified malicious address(es) and/ormalware at S702. In one example, host(s) 414 and/or endpoint(s) 520 inassociation with which malicious address/malware is detected, is/arereferred to as compromised workload(s)/endpoint(s).

At S706, using one or more of sensors 408, server 402 applies an accessprevention scheme to each detected compromised element to prevent accessto network resources by such compromised elements. Such accessprevention scheme can be one a number of available access preventionschemes, each corresponding to a specific policy defined for addressinga different underlying network threat identified in association with acompromised element.

One example access prevention scheme can be to block a particularconnection between a compromised host/workload and one or more specificworkloads in network environment 401. This may be referred to as anetwork element based access prevention scheme. For example, asdescribed with reference to FIG. 4 , connection between host 414 andnetwork element 406-3 may be terminated/blocked.

Another example access prevention scheme can be to quarantine acompromised host/workload as described above. This can be referred to asa quarantine-based access prevention scheme. For example, a policy maybe created based on a threat category and/or source of such category.Such policy may be to quarantine compromised element(s). For example, apolicy may be created to dictate that when an endpoint accesses anadvertisement host with a specific ad-tag (e.g., endpoint 520-1accessing host 526 in FIG. 5 ), and the tag-ad is detected in a datapacket received at network element 406-2 and/or 406-3, then endpoint520-1 should be quarantined. As mentioned such policy may also define aperiod for the quarantine. Such period may be determined based onexperiments and/or empirical studies

Another example access prevention scheme can be such that, depending onthe nature of the underlying malware or threat, access by a compromisedhost/endpoint is blocked to a particular workload on a particularnetwork element 406 while the same compromised workload/endpoint canstill access other workloads on that particular network element 406.This policy may be defined to address situations where a particularcategory of threats or a particular IP address is considered harmful toone workload but not to others. This can be referred to as aworkload-based access prevention scheme. For example, network element406-1 may have multiple applications or workloads running thereon. Anexternal sourced accessed may be a threat to a first application runningon network element 406-1 but not to a second application running onnetwork element 406-1. A policy can be defined to dictate that whileaccess to the first application on network element 406-1 should beblocked, access to the second application on the same network element406-1 should be allowed.

Accordingly, at S706, server 402 dynamically applies an accessprevention scheme based on the nature of the underlying network threat.This dynamic application of access prevention addresses each uniquethreat differently instead of a uniform application of a single type ofaccess prevention to all compromised hosts/endpoints. Accordingly, amore efficient access prevention process is provided that results inmore efficient use and better performance of network environment 401.This advantage is highlighted by the fact that if a uniform accessprevention is applied may connections may be blocked or many endpointsmay be quarantined that would otherwise not be when the nature of theunderlying threat is taken into consideration.

Thereafter, the process reverts back to S700 and S700, S702, S704 andS706 are repeated periodically (or alternatively, continuously) todetect compromised hosts/endpoints and prevent

Next, example device and system configurations are described that can beutilized in the context of the present disclosure as various elements ofsystems of FIGS. 1-5 (e.g., server 402, network elements 406, sensors408, hosts 414, endpoints 520, etc.), to implement examplefunctionalities and processes described with reference to FIGS. 6 and 7.

FIG. 8 illustrates an example computing system, according to one aspectof the present disclosure.

FIG. 8 shows an example of computing system 800, which can be forexample any computing device making up authentication service 415 or anycomponent thereof in which the components of the system are incommunication with each other using connection 805. Connection 805 canbe a physical connection via a bus, or a direct connection intoprocessor 810, such as in a chipset architecture. Connection 805 canalso be a virtual connection, networked connection, or logicalconnection.

In some embodiments computing system 800 is a distributed system inwhich the functions described in this disclosure can be distributedwithin a datacenter, multiple datacenters, a peer network, etc. In someembodiments, one or more of the described system components representsmany such components each performing some or all of the function forwhich the component is described. In some embodiments, the componentscan be physical or virtual devices.

Example computing system 800 includes at least one processing unit (CPUor processor) 810 and connection 805 that couples various systemcomponents including system memory 815, such as read only memory (ROM)820 and random access memory (RAM) 825 to processor 810. Computingsystem 800 can include a cache of high-speed memory 812 connecteddirectly with, in close proximity to, or integrated as part of processor810.

Processor 810 can include any general purpose processor and a hardwareservice or software service, such as services 832, 834, and 836 storedin storage device 830, configured to control processor 810 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. Processor 810 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

To enable user interaction, computing system 800 includes an inputdevice 845, which can represent any number of input mechanisms, such asa microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech, etc. Computingsystem 800 can also include output device 835, which can be one or moreof a number of output mechanisms known to those of skill in the art. Insome instances, multimodal systems can enable a user to provide multipletypes of input/output to communicate with computing system 800.Computing system 800 can include communications interface 840, which cangenerally govern and manage the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

Storage device 830 can be a non-volatile memory device and can be a harddisk or other types of computer readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs), read only memory (ROM), and/or somecombination of these devices.

The storage device 830 can include software services, servers, services,etc., that when the code that defines such software is executed by theprocessor 810, it causes the system to perform a function. In someembodiments, a hardware service that performs a particular function caninclude the software component stored in a computer-readable medium inconnection with the necessary hardware components, such as processor810, connection 805, output device 835, etc., to carry out the function.

For clarity of explanation, in some instances the various embodimentsmay be presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware, and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, rackmount devices, standalone devices, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

The invention claimed is:
 1. A method comprising: receiving a list ofnetwork threats; transforming the list of network threats into a formatcompatible with existing exchange protocols between a server and sensorsof a network; packaging the transformed list into existing data packsexchanged between the server and the sensors; defining policies for thethreats by creating annotations to tag compromised network elements;deploying the defined policies and the existing data packs with thetransformed list of threats feeds to the sensors; collecting continuousfeedback from the sensors on various statistics collected by the sensorson performance and data traffic between their respective networkelements; and updating the policies based upon the collected statistics.2. The method of claim 1, further comprising: detecting, a compromisednetwork element in communication with one or more other networkelements, the compromised network element being associated with at leastone network threat.
 3. The method of claim 2, wherein the compromisednetwork element is an endpoint registered with the network, the endpointhaving accessed an external source having the at least one networkthreat.
 4. The method of claim 2, further comprising: tagging thecompromised network element with a tag from one of the policies.
 5. Themethod of claim 4, further comprising: based on the defined policies,applying by the server through the sensors one of a number of differentaccess prevention schemes to the compromised network element to preventaccess to the network by the compromised network element.
 6. The methodof claim 2, further comprising: based on the defined policies, applyingby the server through the sensors one of a number of different accessprevention schemes to the compromised network element to prevent accessto the network by the compromised network element.
 7. A system,comprising: a non-transitory computer readable media storinginstructions; and a processing component comprising software incombination with electronic computer hardware programmed to cooperatewith the instruction to perform operations including: receive a list ofnetwork threats; transform the list of network threats into a formatcompatible with existing exchange protocols between a server and sensorsof a network; package the transformed list into existing data packsexchanged between the server and the sensors; define policies for thethreats by creating annotations to tag any compromised network elements;deploy the defined policies and the existing data packs with thetransformed list of threats feeds to the sensors; collect continuousfeedback from the sensors on various statistics collected by the sensorson performance and data traffic between their respective networkelements; and update the policies based upon the collected statistics.8. The system of claim 7, the operations further comprising: detecting,a compromised network element in communication with one or more othernetwork elements, the compromised network element being associated withat least one network threat.
 9. The system of claim 8, wherein thecompromised network element is an endpoint registered with the network,the endpoint having accessed an external source having the at least onenetwork threat.
 10. The system of claim 8, the operations furthercomprising: tagging the compromised network element with a tag from oneof the policies.
 11. The system of claim 10, the operations furthercomprising: based on the defined policies, applying by the serverthrough the sensors one of a number of different access preventionschemes to the compromised network element to prevent access to thenetwork by the compromised network element.
 12. The system of claim 8,the operations further comprising: based on the defined policies,applying by the server through the sensors one of a number of differentaccess prevention schemes to the compromised network element to preventaccess to the network by the compromised network element.
 13. Anon-transitory computer readable media storing instructions which whenexecuted by a processor cause the processor to perform operationscomprising: receive a list of network threats; transform the list ofnetwork threats into a format compatible with existing exchangeprotocols between a server and sensors of a network; package thetransformed list into existing data packs exchanged between the serverand the sensors; define policies for the threats by creating annotationsto tag any compromised network elements; deploy the defined policies andthe existing data packs with the transformed list of threats feeds tothe sensors; collect continuous feedback from the sensors on variousstatistics collected by the sensors on performance and data trafficbetween their respective network elements; and update the policies basedupon the collected statistics.
 14. The non-transitory computer readablemedia of claim 13, the operations further comprising: detecting, acompromised network element in communication with one or more othernetwork elements, the compromised network element being associated withat least one network threat.
 15. The non-transitory computer readablemedia of claim 14, wherein the compromised network element is anendpoint registered with the network, the endpoint having accessed anexternal source having the at least one network threat.
 16. Thenon-transitory computer readable media of claim 14, the operationsfurther comprising: tagging the compromised network element with a tagfrom one of the policies.
 17. The non-transitory computer readable mediaof claim 16, the operations further comprising: based on the definedpolicies, applying by the server through the sensors one of a number ofdifferent access prevention schemes to the compromised network elementto prevent access to the network by the compromised network element. 18.The non-transitory computer readable media of claim 14, the operationsfurther comprising: based on the defined policies, applying by theserver through the sensors one of a number of different accessprevention schemes to the compromised network element to prevent accessto the network by the compromised network element.